Symbolic-to-statistical hybridization: extending generation-heavy machine translation

The last few years have witnessed an increasing interest in hybridizing surface-based statistical approaches and rule-based symbolic approaches to machine translation (MT). Much of that work is focused on extending statistical MT systems with symbolic knowledge and components. In the brand of hybridization discussed here, we go in the opposite direction: adding statistical bilingual components to a symbolic system. Our base system is Generation-heavy machine translation (GHMT), a primarily symbolic asymmetrical approach that addresses the issue of Interlingual MT resource poverty in source-poor/target-rich language pairs by exploiting symbolic and statistical target-language resources. GHMT’s statistical components are limited to target-language models, which arguably makes it a simple form of a hybrid system. We extend the hybrid nature of GHMT by adding statistical bilingual components. We also describe the details of retargeting it to Arabic–English MT. The morphological richness of Arabic brings several challenges to the hybridization task. We conduct an extensive evaluation of multiple system variants. Our evaluation shows that this new variant of GHMT—a primarily symbolic system extended with monolingual and bilingual statistical components—has a higher degree of grammaticality than a phrase-based statistical MT system, where grammaticality is measured in terms of correct verb-argument realization and long-distance dependency translation.     

Intercontinental Grids: An Infrastructure for Demand-Driven Innovation

This paper charts the evolution of an intercontinental Grid—INWA—from its first operation connecting Australia and Scotland; its subsequent extension to China; and its use to demonstrate the first large-scale research and education network for the Asia-Pacific region. The paper focuses on the gap between e-Science and e-Social Science arguing that the Grid topology is more compatible with the socio-legal demands of large-scale study of society than more dynamically distributed approaches, such as Cloud Computing. Foundational texts on Grid Computing and its appropriation by research programmes in the UK, USA and China have helped create a positive, symbolic value for Grid Computing. For INWA, this value helped when communicating the aims of the project to potential collaborators and so created the conditions for high-quality, socio-economic data to be placed in a collaborative, analytical environment. There is no equivalent symbolic value for Cloud Computing with potential consequences for its usefulness in establishing such collaborations in future.     

GSTE is partitioned model checking

Verifying whether an ω-regular property is satisfied by a finite-state system is a core problem in model checking. Standard techniques build an automaton with the complementary language, compute its product with the system, and then check for emptiness. Generalized symbolic trajectory evaluation (GSTE) has been recently proposed as an alternative approach, extending the computationally efficient symbolic trajectory evaluation (STE) to general ω-regular properties. In this paper, we show that the GSTE algorithms are essentially a partitioned version of standard symbolic model-checking (SMC) algorithms, where the partitioning is driven by the property under verification. We export this technique of property-driven partitioning to SMC and show that it typically does speed up SMC algorithms. A shorter version of this paper has been presented at CAV’04 (R. Sebastiani et al., Lecture Notes in Comput. Sci., vol. 3114, pp. 143–160, 2004). R. Sebastiani supported in part by the CALCULEMUS! IHP-RTN EC project, code HPRN-CT-2000-00102, by a MIUR COFIN02 project, code 2002097822_003, and by a grant from the Intel Corporation. M.Y. Vardi supported in part by NSF grants CCR-9988322, CCR-0124077, CCR-0311326, IIS-9908435, IIS-9978135, EIA-0086264, and ANI-0216467 by BSF grant 9800096, and by a grant from the Intel Corporation.     

Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series

We investigate the potential of the analysis of noisy non-stationary time series by quantising it into streams of discrete symbols and applying finite-memory symbolic predictors. Careful quantisation can reduce the noise in the time series to make model estimation more amenable. We apply the quantisation strategy in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 on a daily basis, based on predictions of the daily volatility differences in the underlying indexes. We propose a parametric, data-driven quantisation scheme which transforms temporal patterns in the series of daily volatility changes into grammatical and statistical patterns in the corresponding symbolic streams. As symbolic predictors operating on the quantised streams, we use the classical fixed-order Markov models, variable memory length Markov models and a novel variation of fractal-based predictors, introduced in its original form in Tin_ o and Dorffner [1]. The fractal-based predictors are designed to efficiently use deep memory. We compare the symbolic models with continuous techniques such as time-delay neural networks with continuous and categorical outputs, and GARCH models. Our experiments strongly suggest that the robust information reduction achieved by quantising the real-valued time series is highly beneficial. To deal with non-stationarity in financial daily time series, we propose two techniques that combine ‘sophisticated’ models fitted on the training data with a fixed set of simple-minded symbolic predictors not using older (and potentially misleading) data in the training set. Experimental results show that by quantising the volatility differences and then using symbolic predictive models, market makers can sometimes generate a statistically significant excess profit. We also mention some interesting observations regarding the memory structure in the series of daily volatility differences studied.     

Symbolic data analysis tools for recommendation systems

Recommender systems have become an important tool to cope with the information overload problem by acquiring data about user behavior. After tracing the user’s behavior, through actions or rates, computational recommender systems use information- filtering techniques to recommend items. In order to recommend new items, one of the three major approaches is generally adopted: content-based filtering, collaborative filtering, or hybrid filtering. This paper presents three information-filtering methods, each of them based on one of these approaches. In our methods, the user profile is built up through symbolic data structures and the user and item correlations are computed through dissimilarity functions adapted from the symbolic data analysis (SDA) domain. The use of SDA tools has improved the performance of recommender systems, particularly concerning the find good items task measured by the half-life utility metric, when there is not much information about the user.     

Symbolic approximation: an approach to verification in the large

This article describes symbolic approximation, a theoretical foundation for techniques evolved for large-scale verification – in particular, for post hoc verification of the C code in large-scale open-source projects such as the Linux kernel. The corresponding toolset’s increasing maturity means that it is now capable of treating millions of lines of C code source in a few hours on very modest support platforms. In order to explicitly manage the state-space-explosion problem that bedevils model-checking approaches, we work with approximations to programs in a symbolic domain where approximation has a well-defined meaning. A more approximate program means being able to say less about what the program does, which means weaker logic for reasoning about the program. So we adjust the approximation by adjusting the applied logic. That guarantees a safe approximation (one which may generate false alarms but no false negatives) provided the logic used is weaker than the exact logic of C. We choose the logic to suit the analysis.     

To hear—to say: the mediating presence of the healing witness

Illness and trauma challenge self-narratives. Traumatized individuals, unable to speak about their experiences, suffer in isolation. In this paper, I explore Kristeva’s theories of the speaking subject and signification, with its symbolic and semiotic modalities, to understand how a person comes to speak the unspeakable. In discussing the origin of the speaking subject, Kristeva employs Plato’s chora (related to choreo, “to make room for”). The chora reflects the mother’s preparation of the child’s entry into language and forms an interior darkroom, the reservoir of lived experience, from which self-narratives issue. Unable to speak of their suffering, traumatized individuals need someone to help them make room for a time of remembrance, someone who is a willing and capable listener. I call such a person a healing witness. Through the mediating presence of the healing witness, fragmented memories of trauma are recreated and incorporated into self-narratives that are sharable with others. Unfortunately, opportunities for witnessing are vanishing. In the last section, I examine the failure of modern media and communication technologies to bear (“hold,” “carry,” “transport”) acts of witnessing. I argue that they perturb the semiotic. According to Kristeva, meaning arises from the dialectical tension between the semiotic (drives and affects) and the symbolic (logic and rules) and is threatened by arid discourse, psychosomatic illnesses, and outbreaks of violence when the semiotic is not represented. Unless we open technology to the imaginary, we risk losing the capacity to bear witness to one another and to create narratives and connections that are meaningful.     

Symbolic execution with abstraction

We address the problem of error detection for programs that take recursive data structures and arrays as input. Previously we proposed a combination of symbolic execution and model checking for the analysis of such programs: we put a bound on the size of the program inputs and/or the search depth of the model checker to limit the search state space. Here we look beyond bounded model checking and consider state matching techniques to limit the state space. We describe a method for examining whether a symbolic state that arises during symbolic execution is subsumed by another symbolic state. Since the number of symbolic states may be infinite, subsumption is not enough to ensure termination. Therefore, we also consider abstraction techniques for computing and storing abstract states during symbolic execution. Subsumption checking determines whether an abstract state is being revisited, in which case the model checker backtracks—this enables analysis of an under-approximation of the program behaviors. We illustrate the technique with abstractions for lists and arrays. We also discuss abstractions for more general data structures. The abstractions encode both the shape of the program heap and the constraints on numeric data. We have implemented the techniques in the Java PathFinder tool and we show their effectiveness on Java programs. This paper is an extended version of Anand et al. (Proceedings of SPIN, pp. 163–181, 2006).     

Tangibles for learning: a representational analysis of physical manipulation

Manipulatives—physical learning materials such as cubes or tiles—are prevalent in educational settings across cultures and have generated substantial research into how actions with physical objects may support children’s learning. The ability to integrate digital technology into physical objects—so-called ‘digital manipulatives’—has generated excitement over the potential to create new educational materials. However, without a clear understanding of how actions with physical materials lead to learning, it is difficult to evaluate or inform designs in this area. This paper is intended to contribute to the development of effective tangible technologies for children’s learning by summarising key debates about the representational advantages of manipulatives under two key headings: offloading cognition—where manipulatives may help children by freeing up valuable cognitive resources during problem solving, and conceptual metaphors—where perceptual information or actions with objects have a structural correspondence with more symbolic concepts. The review also indicates possible limitations of physical objects—most importantly that their symbolic significance is only granted by the context in which they are used. These arguments are then discussed in light of tangible designs drawing upon the authors’ current research into tangibles and young children’s understanding of number.     

The yellow line: a critical study of the symbolic value of artefacts in health care teams

Artefacts reveal an organisation’s “inner life” and they contribute to its image and reputation. They also play a decisive role for an organisation’s development. In this article, similar artefacts from two different health care teams—a trauma team and a psychiatric team—in the same hospital, are compared. The team members were interviewed and their work observed over the course of several years. It was demonstrated that identical pieces of equipment in a trauma team and a psychiatric team signalled opposite values. The psychiatric team was backward-looking, conservative and contradictory. Modern technology and pieces of equipment were associated with an abandoned and previously criticised activity. The corresponding equipment in the trauma team, on the other hand, signalled a forward looking, developing and unified culture. The trauma team was a relatively new and powerfully idealised phenomenon, which attracted attention. The analysis points out how the symbolic values signal that one activity is attractive and pleasing while another has a low external legitimacy.     

3D content-based search using sketches

This paper presents a novel interactive framework for 3D content-based search and retrieval using as query model an object that is dynamically sketched by the user. In particular, two approaches are presented for generating the query model. The first approach uses 2D sketching and symbolic representation of the resulting gestures. The second utilizes non-linear least squares minimization to model the 3D point cloud that is generated by the 3D tracking of the user’s hands, using superquadrics. In the context of the proposed framework, three interfaces were integrated to the sketch-based 3D search system including (a) an unobtrusive interface that utilizes pointing gesture recognition to allow the user manipulate objects in 3D, (b) a haptic–VR interface composed by 3D data gloves and a force feedback device, and (c) a simple air–mouse. These interfaces were tested and comparative results were extracted according to usability and efficiency criteria.     

Symbolic Graphs: Linear Solutions to Connectivity Related Problems

The importance of symbolic data structures such as Ordered Binary Decision Diagrams (OBDD) is rapidly growing in many areas of Computer Science where the large dimensions of the input models is a challenging feature: OBDD based graph representations allowed to define truly new standards in the achievable dimensions for the Model Checking verification technique. However, OBDD representations pose strict constraints in the algorithm design issue. For example, Depth-First Search (DFS) is not feasible in a symbolic framework and, consequently, many state-of-the-art DFS based algorithms (e.g., connectivity procedures) cannot be easily rearranged to work on symbolically represented graphs. We devise here a symbolic algorithmic strategy, based on the new notion of spine-set, that is general enough to be the engine of linear symbolic step algorithms for both strongly connected components and biconnected components. Our procedures improve on previously designed connectivity symbolic algorithms. Moreover, by an application to the so-called “bad cycle detection problem”, our technique can be used to efficiently solve the emptiness problem for various kinds of ω-automata. This work is a revised and extended version of [22,23]. It is partially supported by the projects PRIN 2005015491 and BIOCHECK.     

Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.     

New Results on the Most Significant Bit of Integer Multiplication

Integer multiplication as one of the basic arithmetic functions has been in the focus of several complexity theoretical investigations and ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for Boolean functions. Analyzing the limits of symbolic graph algorithms for the reachability problem Sawitzki (Proc. of LATIN, LNCS, vol. 3887, pp. 781–792, Springer, Berlin, 2006) has presented the first exponential lower bound on the π-OBDD size for the most significant bit of integer multiplication according to one predefined variable order π. Since the choice of the variable order is a main issue to obtain OBDDs of small size the investigation is continued. As a result a new upper bound method and the first non-trivial upper bound on the size of OBDDs according to an arbitrary variable order is presented. Furthermore, Sawitzki’s lower bound is improved.     

Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed algorithm examines the person’s data over these 30 days and automatically extracts the person’s daily pattern.     

User diversity and design representation: Towards increased effectiveness in Design for All

This paper addresses user modelling for “Design for All” in a model-based approach to Human-Computer Interaction, paying particular attention to placing user models within organisational role- and task-related contexts. After reviewing a variety of user modelling approaches, and deriving requirements for user modelling related to Design for All, the paper proposes a role-driven individualised approach. Such an approach is based on a model-based representation schema and a unifying notation that keeps the user’s models and the contextual information transparent and consistent. Individualisation is achieved by coupling symbolic model specifications with neural networking on synchronisation links between symbolic representation elements. As a result, user modelling for Design for All is achieved not by stereotypical user properties and functional roles, but by accommodating the actual users’ behaviour. Published online: 18 May 2001     

The saturation algorithm for symbolic state-space exploration

We present various algorithms for generating the state space of an asynchronous system based on the use of multiway decision diagrams to encode sets and Kronecker operators on boolean matrices to encode the next-state function. The Kronecker encoding allows us to recognize and exploit the “locality of effect” that events might have on state variables. In turn, locality information suggests better iteration strategies aimed at minimizing peak memory consumption. In particular, we focus on the saturation strategy, which is completely different from traditional breadth-first symbolic approaches, and extend its applicability to models where the possible values of the state variables are not known a priori. The resulting algorithm merges “on-the-fly” explicit state-space generation of each submodel with symbolic state-space generation of the overall model. Each algorithm we present is implemented in our tool SmArT. This allows us to run fair and detailed comparisons between them on a suite of representative models. Saturation, in particular, is shown to be many orders of magnitude more efficient in terms of memory and time with respect to traditional methods.     

Object-Oriented Symbolic Derivation and Automatic Programming of Finite Elements in Mechanics

Symbolic approaches to assist in the development of finite element formulations have been used since the late 1970s. Today, symbolic mathematical software such as Mathematica, Maple, etc., has proved to be helpful when testing formulations. In earlier work, the authors introduced a new way of integrating naturally symbolic concepts in numerical finite element codes, taking advantage of an objectoriented code organization. In this paper, we wish to prove on practical examples that the proposed approach is very attractive and promising today, leading to an alternative way of conceiving finite element codes. After presenting a state-of- the-art of symbolic approaches for finite element developments, we first give a practical application of symbolic developments (for discontinuous space-time formulations), and then examples of Computer Aided Software Engineering tools that can be introduced into such a finite element environment.     

Symbolic Techniques in Satisfiability Solving

Recent work has shown how to use binary decision diagrams for satisfiability solving. The idea of this approach, which we call symbolic quantifier elimination, is to view an instance of propositional satisfiability as an existentially quantified proposition formula. Satisfiability solving then amounts to quantifier elimination; once all quantifiers have been eliminated, we are left with either 1 or 0. Our goal in this work is to study the effectiveness of symbolic quantifier elimination as an approach to satisfiability solving. To that end, we conduct a direct comparison with the DPLL-based ZChaff, as well as evaluate a variety of optimization techniques for the symbolic approach. In comparing the symbolic approach to ZChaff, we evaluate scalability across a variety of classes of formulas. We find that no approach dominates across all classes. While ZChaff dominates for many classes of formulas, the symbolic approach is superior for other classes of formulas. Once we have demonstrated the viability of the symbolic approach, we focus on optimization techniques for this approach. We study techniques from constraint satisfaction for finding a good plan for performing the symbolic operations of conjunction and of existential quantification. We also study various variable-ordering heuristics, finding that while no heuristic seems to dominate across all classes of formulas, the maximum-cardinality search heuristic seems to offer the best overall performance. ★A preliminary version of the paper was presented in SAT'04. Supported in part by NSF grants CCR-9988322, CCR-0124077, CCR-0311326, IIS-9908435, IIS-9978135, EIA-0086264, ANI-0216467, and by BSF grant 9800096.